Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.1 KiB
Average record size in memory100.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with invoice_ammt and 5 other fieldsHigh correlation
recency_days is highly correlated with invoice_ammtHigh correlation
invoice_ammt is highly correlated with gross_revenue and 2 other fieldsHigh correlation
item_ammt is highly correlated with gross_revenue and 5 other fieldsHigh correlation
n_of_dif_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with invoice_ammtHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtt_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422364) Skewed
qtt_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtt_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-09-13 11:23:24.736668
Analysis finished2022-09-13 11:24:05.931520
Duration41.19 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:06.102453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2022-09-13T12:24:06.277410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26541
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
26461
 
< 0.1%
5991
 
< 0.1%
26481
 
< 0.1%
6011
 
< 0.1%
6031
 
< 0.1%
51441
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2022-09-13T12:24:06.466349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2022-09-13T12:24:06.642293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
181641
 
< 0.1%
129331
 
< 0.1%
129351
 
< 0.1%
149841
 
< 0.1%
170331
 
< 0.1%
137041
 
< 0.1%
129391
 
< 0.1%
170371
 
< 0.1%
141251
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2963
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.321711
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:06.837218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.62331
Coefficient of variation (CV)3.848448607
Kurtosis353.944724
Mean2749.321711
Median Absolute Deviation (MAD)672.16
Skewness16.77755612
Sum8162736.16
Variance111949589.6
MonotonicityNot monotonic
2022-09-13T12:24:07.001179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.652
 
0.1%
533.332
 
0.1%
745.062
 
0.1%
734.942
 
0.1%
731.92
 
0.1%
3312
 
0.1%
719.781
 
< 0.1%
13375.871
 
< 0.1%
447.641
 
< 0.1%
567.361
 
< 0.1%
Other values (2953)2953
99.5%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28763894
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:07.192117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75677911
Coefficient of variation (CV)1.209513686
Kurtosis2.777962659
Mean64.28763894
Median Absolute Deviation (MAD)26
Skewness1.798379538
Sum190870
Variance6046.116697
MonotonicityNot monotonic
2022-09-13T12:24:07.370061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

invoice_ammt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723139104
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:07.572983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85653132
Coefficient of variation (CV)1.547495379
Kurtosis190.8344494
Mean5.723139104
Median Absolute Deviation (MAD)2
Skewness10.76680458
Sum16992
Variance78.43814702
MonotonicityNot monotonic
2022-09-13T12:24:07.757925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

item_ammt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.852476
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:07.956860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578045
Coefficient of variation (CV)3.659489067
Kurtosis465.998084
Mean1608.852476
Median Absolute Deviation (MAD)422
Skewness17.85859125
Sum4776683
Variance34663575.24
MonotonicityNot monotonic
2022-09-13T12:24:08.148812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2888
 
0.3%
2728
 
0.3%
848
 
0.3%
2468
 
0.3%
2608
 
0.3%
4937
 
0.2%
1347
 
0.2%
Other values (1661)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

n_of_dif_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7241495
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:08.358733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8964081
Coefficient of variation (CV)2.199211884
Kurtosis354.8611303
Mean122.7241495
Median Absolute Deviation (MAD)44
Skewness15.70763473
Sum364368
Variance72844.07112
MonotonicityNot monotonic
2022-09-13T12:24:08.544672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2629
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2000
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.89770967
Minimum2.15
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:08.737612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.918
Q113.12
median17.96
Q324.99
95-th percentile90.498
Maximum56157.5
Range56155.35
Interquartile range (IQR)11.87

Descriptive statistics

Standard deviation1036.934408
Coefficient of variation (CV)19.98035009
Kurtosis2890.707127
Mean51.89770967
Median Absolute Deviation (MAD)5.98
Skewness53.44422364
Sum154084.3
Variance1075232.966
MonotonicityNot monotonic
2022-09-13T12:24:08.905570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.497
 
0.2%
16.926
 
0.2%
17.666
 
0.2%
19.066
 
0.2%
16.396
 
0.2%
16.826
 
0.2%
16.535
 
0.2%
18.845
 
0.2%
105
 
0.2%
17.135
 
0.2%
Other values (1990)2912
98.1%
ValueCountFrequency (%)
2.151
< 0.1%
2.431
< 0.1%
2.461
< 0.1%
2.511
< 0.1%
2.521
< 0.1%
2.651
< 0.1%
2.661
< 0.1%
2.711
< 0.1%
2.761
< 0.1%
2.771
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.991
< 0.1%
872.131
< 0.1%
841.021
< 0.1%
651.171
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.34851138
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:09.099508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92307692
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.41025641

Descriptive statistics

Standard deviation63.54492876
Coefficient of variation (CV)0.9435238799
Kurtosis4.887109087
Mean67.34851138
Median Absolute Deviation (MAD)26.28571429
Skewness2.062770925
Sum199957.7303
Variance4037.957972
MonotonicityNot monotonic
2022-09-13T12:24:09.280450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
516
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06327807795
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:09.478374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.02941176471
Q30.05540166205
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03762388427

Descriptive statistics

Standard deviation0.1344820641
Coefficient of variation (CV)2.125255198
Kurtosis121.5575473
Mean0.06327807795
Median Absolute Deviation (MAD)0.01433823529
Skewness8.773259386
Sum187.8726134
Variance0.01808542557
MonotonicityNot monotonic
2022-09-13T12:24:09.651318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.166666666721
 
0.7%
0.333333333321
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.416
 
0.5%
0.2515
 
0.5%
0.0238095238115
 
0.5%
0.0357142857115
 
0.5%
Other values (1340)2794
94.1%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%
0.83333333331
 
< 0.1%
0.751
 
< 0.1%
0.666666666712
0.4%
0.65147453081
 
< 0.1%
0.61
 
< 0.1%

qtt_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1569552
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:09.846270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)24.33349783
Kurtosis2765.52864
Mean62.1569552
Median Absolute Deviation (MAD)1
Skewness51.79774426
Sum184544
Variance2287644.557
MonotonicityNot monotonic
2022-09-13T12:24:10.049205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8137641
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:10.536037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.3333333
Q3281.6923077
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.4423077

Descriptive statistics

Standard deviation791.5551894
Coefficient of variation (CV)3.168581172
Kurtosis2255.538236
Mean249.8137641
Median Absolute Deviation (MAD)83.08333333
Skewness44.67271661
Sum741697.0657
Variance626559.6179
MonotonicityNot monotonic
2022-09-13T12:24:10.734986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
739
 
0.3%
869
 
0.3%
1368
 
0.3%
758
 
0.3%
888
 
0.3%
608
 
0.3%
1637
 
0.2%
Other values (1969)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct268
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.118282714
Minimum0.1764705882
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-09-13T12:24:10.966913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1764705882
5-th percentile0.95
Q11.8
median2.75
Q34
95-th percentile6.5
Maximum16
Range15.82352941
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.833753643
Coefficient of variation (CV)0.5880652306
Kurtosis3.641104541
Mean3.118282714
Median Absolute Deviation (MAD)1.083333333
Skewness1.430692644
Sum9258.181378
Variance3.362652424
MonotonicityNot monotonic
2022-09-13T12:24:11.155853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3204
 
6.9%
2200
 
6.7%
4163
 
5.5%
5142
 
4.8%
3.5136
 
4.6%
4.5113
 
3.8%
2.5111
 
3.7%
682
 
2.8%
3.33333333371
 
2.4%
171
 
2.4%
Other values (258)1676
56.4%
ValueCountFrequency (%)
0.17647058821
 
< 0.1%
0.22110552761
 
< 0.1%
0.27272727271
 
< 0.1%
0.27669902911
 
< 0.1%
0.27906976741
 
< 0.1%
0.28205128211
 
< 0.1%
0.33064516131
 
< 0.1%
0.33333333334
0.1%
0.34020618561
 
< 0.1%
0.36263736261
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
143
 
0.1%
13.51
 
< 0.1%
121
 
< 0.1%
119
 
0.3%
107
 
0.2%
9.51
 
< 0.1%
916
0.5%
8.52
 
0.1%
834
1.1%

Interactions

2022-09-13T12:24:01.998777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:28.694405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:31.473530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:34.494551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:37.327647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:40.113771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:42.502007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:45.216139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:48.149190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:51.008276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:53.687432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:56.427557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:59.132693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:02.203712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:28.947323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:31.704456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:34.676506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:37.544577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:40.292712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:42.707941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:45.422074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:48.418103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:51.208225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:54.073311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:56.629492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:59.336628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:02.402648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:29.202256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:31.909386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:34.993392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:37.736517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:40.460658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:42.903865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:45.753968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:48.673024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:54.264236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:56.825430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:59.535565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:02.602584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:54.451190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:59.734501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:02.809519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:29.596129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:40.820544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:43.310748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:46.170835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:51.785028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:54.651113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:57.229301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:59.943434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:02.994459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:29.766064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:32.616152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:35.554226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:40.987490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:43.496688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:54.822058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:57.422240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:00.162352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:03.213390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:29.975009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:32.897064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:35.759147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:38.535274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:41.179416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:43.719605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:46.576704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:49.631728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:52.184901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:55.029991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:57.638157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:00.438262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:03.427321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:30.185928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:33.165977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:35.976092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:38.745206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:41.371353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:43.941546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:46.797621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:49.835651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:52.396847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:55.234938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:57.854088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:00.672188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:03.825180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:30.383864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:33.355916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:36.206004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:38.938145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:41.545312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:44.145481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:47.019563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:50.015594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:52.586772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:55.414867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:58.051038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:00.881134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:04.034127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:30.585801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:33.548869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:36.406954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:39.142069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:23:36.601893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-09-13T12:24:01.326992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:04.586937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:30.997682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:34.065688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:36.824806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:39.691891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:42.105119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:44.781278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:47.673354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:50.601422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:53.197576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:56.017688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:58.683837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:01.545922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:04.879843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:31.239593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:34.283632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:37.079727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:39.904824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:42.306055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:44.998209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:47.905281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:50.802344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:53.440499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:56.223622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:23:58.906754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-13T12:24:01.775834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-09-13T12:24:11.359773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-13T12:24:11.709674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-13T12:24:12.056563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-13T12:24:12.405440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-13T12:24:05.314704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-13T12:24:05.744567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_ammtitem_ammtn_of_dif_productsavg_ticketavg_recency_daysfrequencyqtt_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.1535.5000000.48611140.050.9705880.176471
11130473232.5956.09.01390.0171.018.9027.2500000.04878035.0154.4444441.222222
22125836705.382.015.05028.0232.028.9023.1875000.04569950.0335.2000001.600000
3313748948.2595.05.0439.028.033.8792.6666670.0179210.087.8000001.600000
4415100876.00333.03.080.03.0292.008.6000000.13636422.026.6666670.666667
55152914623.3025.014.02102.0102.045.3323.2000000.05444129.0150.1428571.214286
66146885630.877.021.03621.0327.017.2218.3000000.073569399.0172.4285711.142857
77178095411.9116.012.02057.061.088.7235.7000000.03910641.0171.4166671.916667
881531160767.900.091.038194.02379.025.544.1444440.315508474.0419.7142860.472527
99160982005.6387.07.0613.067.029.9347.6666670.0243900.087.5714292.142857

Last rows

df_indexcustomer_idgross_revenuerecency_daysinvoice_ammtitem_ammtn_of_dif_productsavg_ticketavg_recency_daysfrequencyqtt_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.066.00.2857146.0645.00000011.0
2960563717232421.522.02.0203.036.011.7112.00.1538460.0101.5000005.0
2961563817468137.0010.02.0116.05.027.404.00.4000000.058.0000001.0
2962564913596697.045.02.0406.0166.04.207.00.2500000.0203.0000005.0
29635655148931237.859.02.0799.073.016.962.00.6666670.0399.5000007.0
2964565912479473.2011.01.0382.030.015.774.00.33333334.0382.0000008.0
2965568014126706.137.03.0508.015.047.083.01.00000050.0169.3333332.0
29665686135211092.391.03.0733.0435.02.514.50.3000000.0244.3333333.0
2967569615060301.848.04.0262.0120.02.521.02.0000000.065.5000002.0
2968571512558269.967.01.0196.011.024.546.00.285714196.0196.0000005.0